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중·고등 교원의 직무환경과 작업 관련 근골격계 증상 유병률 관계에서 스트레스가 직·간접적으로 미치는 영향
손상우,황병준 대한정형도수물리치료학회 2022 대한정형도수물리치료학회지 Vol.28 No.1
Background: The aim of the study is to examine whether mediating or moderating effects of stress between teachers’ work environment and work-related musculoskeletal disorders. Methods: Two hundred nine participants took part in the study and completed the surveys including work environment, stress and work-related musculoskeletal disorders questionnaires. Hayes’s PROCESS macro was used to test the research models for mediating and moderating effects of stress between work environment with teachers and work-related musculoskeletal disorders. Indirect effect was tested using bootstrapped confidence intervals. Results: The result confirmed that Stress served as a indirect mediator between work environment and work-related musculoskeletal disorders, whereas there was no significant the moderating effect. Conclusion: Stress mediates the relationship between work environment and work-related musculoskeletal disorders. Therefore, it is crucial that teachers’ work environment that increases stress should be enhanced to reduce work-related musculoskeletal disorders.
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LLM 기반 VOC 데이터 자동 분류 및 Action Mapping 적용 사례 연구
손상우,송지훈 한국산업융합학회 2025 한국산업융합학회 논문집 Vol.28 No.4
This study proposes a novel methodology for analyzing Voice of Customer (VOC) data using a Large Language Model(LLM), and evaluates its efficiency and practical applicability. VOC refers to the structured analysis of customer feedback to improve products and services, serving as a key resource for enhancing customer satisfaction and achieving competitive advantage in modern business. Using real-world VOC survey data from 2022 to 2024, our study focused on analyzing unstructured textual responses. To address the time consumption and complexity of traditional codebook-based manual classification, the study simplified the existing classification system and performed automated categorization using LLM. Furthermore, an Action Mapping Matrix was constructed for key VOC issues, enabling quantitative prioritization and strategic response planning. The LLM-based analysis processed a total of 2,604,282 tokens and was completed in approximately 105 minutes at a cost of 2,157 KRW, demonstrating significant time and cost savings compared to manual methods. The findings indicate that LLM-based VOC analysis can deliver practical efficiency for repetitive and structured tasks, providing a foundation for strategic AI adoption in both public and private sectors.
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